The internet of things for healthcare: optimising e-health system availability in the fog and cloud

Santos, Guto Leoni; Gomes, Demis; Kelner, Judith; Sadok, Djamel; Silva, Francisco Airton; Endo, Patrícia Takako; Lynn, Theo

doi:10.1504/ijcse.2020.106873

Cited by 18 publications

(17 citation statements)

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“…Both time and resource capacities are detailed. The parameters were extracted from the literature ( [26,[48][49][50]).…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Strielkina et al [5] presented in [5,25] two of the first studies adopting queuing network models and Markov models respectively for the availability evaluation of IoT healthcare systems considering attacks on vulnerabilities. Santos et al [26] proposed using a multi-objective optimization algorithm, NSGA-II, in combination with stochastic models, to optimize the system availability of a fog-cloud based IoT for healthcare. Pereira et al [27] in their most recent work proposed comprehensive continuous time Markov chain (CTMC) models to investigate the availability of fog/edge computing nodes for drone-based facial recognition security systems.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, none of the works considered the data transactions throughout the computing systems in the medical centers. Santos et al [26] presented a hierarchical model of a reliability block diagram (RBD) with a stochastic Petri Net (SPN) and a surrogate model to analyze and maximize the availability of an IoT infrastructure for healthcare associated with cloud-fog computing paradigms. Nevertheless, performance metrics were not considered in the work.…”

Section: Related Workmentioning

confidence: 99%

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Performability Evaluation of Load Balancing and Fail-over Strategies for Medical Information Systems with Edge/Fog Computing Using Stochastic Reward Nets

Nguyen

Fé

Brito

et al. 2021

Sensors

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The aggressive waves of ongoing world-wide virus pandemics urge us to conduct further studies on the performability of local computing infrastructures at hospitals/medical centers to provide a high level of assurance and trustworthiness of medical services and treatment to patients, and to help diminish the burden and chaos of medical management and operations. Previous studies contributed tremendous progress on the dependability quantification of existing computing paradigms (e.g., cloud, grid computing) at remote data centers, while a few works investigated the performance of provided medical services under the constraints of operational availability of devices and systems at local medical centers. Therefore, it is critical to rapidly develop appropriate models to quantify the operational metrics of medical services provided and sustained by medical information systems (MIS) even before practical implementation. In this paper, we propose a comprehensive performability SRN model of an edge/fog based MIS for the performability quantification of medical data transaction and services in local hospitals or medical centers. The model elaborates different failure modes of fog nodes and their VMs under the implementation of fail-over mechanisms. Sophisticated behaviors and dependencies between the performance and availability of data transactions are elaborated in a comprehensive manner when adopting three main load-balancing techniques including: (i) probability-based, (ii) random-based and (iii) shortest queue-based approaches for medical data distribution from edge to fog layers along with/without fail-over mechanisms in the cases of component failures at two levels of fog nodes and fog virtual machines (VMs). Different performability metrics of interest are analyzed including (i) recover token rate, (ii) mean response time, (iii) drop probability, (iv) throughput, (v) queue utilization of network devices and fog nodes to assimilate the impact of load-balancing techniques and fail-over mechanisms. Discrete-event simulation results highlight the effectiveness of the combination of these for enhancing the performability of medical services provided by an MIS. Particularly, performability metrics of medical service continuity and quality are improved with fail-over mechanisms in the MIS while load balancing techniques help to enhance system performance metrics. The implementation of both load balancing techniques along with fail-over mechanisms provide better performability metrics compared to the separate cases. The harmony of the integrated strategies eventually provides the trustworthiness of medical services at a high level of performability. This study can help improve the design of MIS systems integrated with different load-balancing techniques and fail-over mechanisms to maintain continuous performance under the availability constraints of medical services with heavy computing workloads in local hospitals/medical centers, to combat with new waves of virus pandemics.

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“…Both time and resource capacities are detailed. The parameters were extracted from the literature ( [26,[48][49][50]).…”

Section: Simulation Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Performability Evaluation of Load Balancing and Fail-over Strategies for Medical Information Systems with Edge/Fog Computing Using Stochastic Reward Nets

Nguyen

Fé

Brito

et al. 2021

Sensors

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“…SPNs [32][33][34][35][36][37][38] can be identified as a type of directed graph divided into two parts, filled by three types of objects. These objects are places, transitions, and directed arcs that connect places for transitions and transitions to places.…”

Section: Stochastic Petri Netmentioning

confidence: 99%

Offloading Data through Unmanned Aerial Vehicles: A Dependability Evaluation

et al. 2021

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Applications in the Internet of Things (IoT) context continuously generate large amounts of data. The data must be processed and monitored to allow rapid decision making. However, the wireless connection that links such devices to remote servers can lead to data loss. Thus, new forms of a connection must be explored to ensure the system’s availability and reliability as a whole. Unmanned aerial vehicles (UAVs) are becoming increasingly empowered in terms of processing power and autonomy. UAVs can be used as a bridge between IoT devices and remote servers, such as edge or cloud computing. UAVs can collect data from mobile devices and process them, if possible. If there is no processing power in the UAV, the data are sent and processed on servers at the edge or in the cloud. Data offloading throughout UAVs is a reality today, but one with many challenges, mainly due to unavailability constraints. This work proposes stochastic Petri net (SPN) models and reliability block diagrams (RBDs) to evaluate a distributed architecture, with UAVs focusing on the system’s availability and reliability. Among the various existing methodologies, stochastic Petri nets (SPN) provide models that represent complex systems with different characteristics. UAVs are used to route data from IoT devices to the edge or the cloud through a base station. The base station receives data from UAVs and retransmits them to the cloud. The data are processed in the cloud, and the responses are returned to the IoT devices. A sensitivity analysis through Design of Experiments (DoE) showed key points of improvement for the base model, which was enhanced. A numerical analysis indicated the components with the most significant impact on availability. For example, the cloud proved to be a very relevant component for the availability of the architecture. The final results could prove the effectiveness of improving the base model. The present work can help system architects develop distributed architectures with more optimized UAVs and low evaluation costs.

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“…Alguns artigos relacionados abordaram modelos SPN para avaliar sistemas computacionais em saúde com apoio de sensores IoT [Pantelopoulos and Bourbakis 2009], [Araujo et al 2014], [Santos et al 2018a], [Tigre et al 2018], [Santos et al 2018b]. Dentre os trabalhos citados, apenas [Tigre et al 2018] e [Santos et al 2018b] aplicaram análise de sensibilidade sobre os modelos propostos.…”

Section: Introductionunclassified

Modelo Estocástico para Avaliação de Disponibilidade de Hospitais Inteligentes

Rodrigues¹,

Gonçalves²,

Fé³

et al. 2020

Anais Do Workshop Em Desempenho De Sistemas Computacionais E De Comunicação (WPerformance 2020)

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Smart hospitals need local and remote servers to efficiently process and store data. However, there is a significant difficulty in assessing the availability of such systems in real contexts, because failures are not tolerated and the cost of prototyping is high. This paper adopts Stochastic Petri Nets (SPNs) to assess the availability of an smart hospital system, avoiding premature investment in real equipment. In addition, this work presents a sensitivity analysis that identifies the most critical architecture components. The proposed model has the potential to assist hospital systems administrators in planning more optimized architectures according to their needs.

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The internet of things for healthcare: optimising e-health system availability in the fog and cloud

Cited by 18 publications

References 0 publications

Performability Evaluation of Load Balancing and Fail-over Strategies for Medical Information Systems with Edge/Fog Computing Using Stochastic Reward Nets

Performability Evaluation of Load Balancing and Fail-over Strategies for Medical Information Systems with Edge/Fog Computing Using Stochastic Reward Nets

Offloading Data through Unmanned Aerial Vehicles: A Dependability Evaluation

Modelo Estocástico para Avaliação de Disponibilidade de Hospitais Inteligentes

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